Data Mining and Open DataLaajuus (5 cr)
Code: R504D79
Credits
5 op
Teaching language
- English
Objective
The student understands the basic concepts, principles, methods and implementation techniques of data mining. The student outlines the philosophy of open data and is capable to utilize and visualize it.
Content
- Open data
- Pattern recognition in data mining
- Cluster analysis / Unsupervised machine learning
- Data Lakes / Data Fabrics / Data Warehouse
- Text mining / Sentiment analysis (opinion mining / emotion AI)
- Data visualisation in dashboards
- Legislation and ethical issues
Assessment criteria, satisfactory (1)
Student knows the main concept of data mining. Student outlines the principles of open data. The student understands the key legal and ethical issues in open data and data mining context.
Assessment criteria, good (3)
The student understands the foundation of data mining. The student can apply proper implementation methods and techniques for data mining. The student can apply open data in practical application development and take into account key legal and ethical issues.
Assessment criteria, excellent (5)
The student deeply understands the concept, principles, methods and techniques of data mining. The student can utilize and visualize open data in wider practical application development. The student can select proper implementation methods and techniques in challenging data mining application development. The student deeply understands the key legal and ethical issues in open data and data mining context and can take them into account in practice.
Enrollment
13.03.2023 - 31.07.2023
Timing
09.10.2023 - 10.12.2023
Credits
5 op
Mode of delivery
Contact teaching
Unit
Bachelor of Engineering, Information Technology
Teaching languages
- English
Seats
0 - 30
Teachers
- Erkki Mattila
Responsible person
Erkki Mattila
Student groups
-
R54D21SBachelor of Engineering, Machine Learning and Data Engineering (full time studies), 2021
Objective
The student understands the basic concepts, principles, methods and implementation techniques of data mining. The student outlines the philosophy of open data and is capable to utilize and visualize it.
Content
- Open data
- Pattern recognition in data mining
- Cluster analysis / Unsupervised machine learning
- Data Lakes / Data Fabrics / Data Warehouse
- Text mining / Sentiment analysis (opinion mining / emotion AI)
- Data visualisation in dashboards
- Legislation and ethical issues
Location and time
Lapland UAS Rantavitikka Campus during the Autumn term 2023.
Materials
Lecture notes and practices in Moodle workspace and OneDrive cloud
Recommended reading:
Han J. & al. 2022. Data Mining: Concepts and Techniques, 4th Edition. Morgan Kaufmann Publishers
Witten I. H. & al. 2016. Data Mining: Practical Machine Learning Tools and Techniques, 4th Edition. Morgan Kaufmann Publishers
Teaching methods
Lectures and practices 30 h. Self-supervised work 105 h.
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
Student knows the main concept of data mining. Student outlines the principles of open data. The student understands the key legal and ethical issues in open data and data mining context.
Assessment criteria, good (3)
The student understands the foundation of data mining. The student can apply proper implementation methods and techniques for data mining. The student can apply open data in practical application development and take into account key legal and ethical issues.
Assessment criteria, excellent (5)
The student deeply understands the concept, principles, methods and techniques of data mining. The student can utilize and visualize open data in wider practical application development. The student can select proper implementation methods and techniques in challenging data mining application development. The student deeply understands the key legal and ethical issues in open data and data mining context and can take them into account in practice.
Assessment criteria, satisfactory (1-2)
Student knows the main concept of data mining. Student outlines the principles of open data. The student understands the key legal and ethical issues in open data and data mining context.
Assessment criteria, good (3-4)
The student understands the foundation of data mining. The student can apply proper implementation methods and techniques for data mining. The student can apply open data in practical application development and take into account key legal and ethical issues.
Assessment criteria, excellent (5)
The student deeply understands the concept, principles, methods and techniques of data mining. The student can utilize and visualize open data in wider practical application development. The student can select proper implementation methods and techniques in challenging data mining application development. The student deeply understands the key legal and ethical issues in open data and data mining context and can take them into account in practice.